Merging updates to conform to JOSS guidelines (#46)

* Update code examples for paper/paper.md to follow syntax of v0.1.14 * Update to VERSION v0.1.15 * No changes in functionalities in current patch update. Only fixes and additions are made to follow JOSS guidelines. * Remove OhMyREPL and Revise from Project.toml
UCD4IDS · Jan 21, 2022 · 8d12bf2 · 8d12bf2 · zengfung · Jan 21, 2022
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Showing 6 changed files with 8 additions and 8 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "WaveletsExt"
 uuid = "8f464e1e-25db-479f-b0a5-b7680379e03f"
 authors = ["Zeng Fung Liew <[email protected]>", "Shozen Dan <[email protected]>"]
-version = "0.1.14"
+version = "0.1.15"
 
 [deps]
 AverageShiftedHistograms = "77b51b56-6f8f-5c3a-9cb4-d71f9594ea6e"

diff --git a/paper/bestbasis.png b/paper/bestbasis.png
diff --git a/paper/denoising.png b/paper/denoising.png
diff --git a/paper/examples.jl b/paper/examples.jl
@@ -24,7 +24,7 @@ p2 = wiggle(y) |> p -> plot!(p, yticks=1:9, title="Stationary WT")
 
 # Combine and save plot
 p = plot(p1, p2, layout=(1,2))
-savefig(p, "transforms.png")
+savefig(p, "tpaper/ransforms.png")
 
 # 2. Best Basis Algorithms -----------------------------------------------------------------
 #    Compared to Wavelets.jl, WaveletsExt.jl has an extended ability of catering toward
@@ -39,12 +39,12 @@ xw = wpdall(x, wt, 6)
 
 # ----- Joint Best Basis (JBB)
 tree = bestbasistree(xw, JBB())
-p1 = plot_tfbdry(tree, 6, nd_col=:green, ln_col=:black, bg_col=:white) |> 
+p1 = plot_tfbdry(tree, 6, node_color=:green, line_color=:black, background_color=:white) |> 
      p -> plot!(p, title="JBB")
 
 # ----- Least Statistically Dependent Basis (LSDB)
 tree = bestbasistree(xw, LSDB())
-p2 = plot_tfbdry(tree, 6, nd_col=:green, ln_col=:black, bg_col=:white) |> 
+p2 = plot_tfbdry(tree, 6, node_color=:green, line_color=:black, background_color=:white) |> 
      p -> plot!(p, title="LSDB")
 
 # Combine and save plot
@@ -118,7 +118,7 @@ ldb = LocalDiscriminantBasis(wt=wt,
 X̂ = fit_transform(ldb, X, y)
 
 # Plot the best basis for feature extraction
-p2 = plot_tfbdry(ldb.tree, 6, nd_col=:green, ln_col=:black, bg_col=:white)
+p2 = plot_tfbdry(ldb.tree, 6, node_color=:green, line_color=:black, background_color=:white)
 plot!(p2, title="Basis Selection using LDB")
 
 p = plot(p1, p2, size=(600,300))

diff --git a/paper/ldb.png b/paper/ldb.png
diff --git a/paper/paper.md b/paper/paper.md
@@ -71,12 +71,12 @@ xw = wpdall(x, wt, 6)
 
 # ----- Joint Best Basis (JBB)
 tree = bestbasistree(xw, JBB())
-p1 = plot_tfbdry(tree, nd_col=:black, ln_col=:black, bg_col=:white) |> 
+p1 = plot_tfbdry(tree, node_color=:green, line_color=:black, background_color=:white) |> 
      p -> plot!(p, title="JBB")
 
 # ----- Least Statistically Dependent Basis (LSDB)
 tree = bestbasistree(xw, LSDB())
-p2 = plot_tfbdry(tree, nd_col=:black, ln_col=:black, bg_col=:white) |> 
+p2 = plot_tfbdry(tree, node_color=:green, line_color=:black, background_color=:white) |> 
      p -> plot!(p, title="LSDB")
 
 # Combine and save plot
@@ -159,7 +159,7 @@ ldb = LocalDiscriminantBasis(
 X̂ = fit_transform(ldb, X, y)
 
 # Plot the best basis for feature extraction
-p2 = plot_tfbdry(ldb.tree, nd_col=:black, ln_col=:black, bg_col=:white)
+p2 = plot_tfbdry(ldb.tree, node_color=:green, line_color=:black, background_color=:white)
 plot!(p2, title="Basis Selection using LDB")
 
 # Combine and save plot